The present invention relates to neuromorphic and synaptronic computation, and in particular, an event-based neural network with hierarchical addressing.
Neuromorphic and synaptronic computation, also referred to as artificial neural networks, are computational systems that permit electronic systems to essentially function in a manner analogous to that of biological brains. In traditional von Neumann architectures, memory and computation are separated. By comparison, embodiments of the invention utilize biologically inspired architecture where threshold based computation is integrated with memory. In neuromorphic and synaptronic computation, connections are created between processing elements that are roughly functionally equivalent to neurons of a biological brain. Neuromorphic and synaptronic computation may comprise various electronic circuits that are modeled on biological neurons.
In biological systems, the point of contact between an axon of a neural module and a dendrite on another neuron is called a synapse, and with respect to the synapse, the two neurons are respectively called pre-synaptic and post-synaptic. The essence of our individual experiences is stored in conductance of the synapses.